Method, system, and computer program product for determining a narcotics use indicator

ABSTRACT

A method, system, and computer program product determines a controlled substance use indicator to enable a physician, or other prescriber, to quickly review a numerical score that reflects a patient&#39;s past drug use and is indicative of proper, or improper, future drug use. This score analyzes many aspects of a patient&#39;s past activities to determine multiple individual indicator values that may be selectively weighted to create a final controlled substance use indicator. Such individual indicator values may include: a usage related indicator factoring in the patient&#39;s past drug use, particularly the type of controlled substances used; an instruction related indicator that may consider the patient&#39;s past use of prescribers, quantity of prescriptions, or the number of open prescriptions from different prescribers; a dispensing related indicator that examines a patient&#39;s use of pharmacies in filling prescriptions; or even an auxiliary indicator that may reflect the patient&#39;s number of active prescriptions.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.14/188,171 filed on Feb. 24, 2014, which is a continuation of U.S.patent application Ser. No. 13/234,777 filed on Sep. 16, 2011, now U.S.Pat. No. 8,688,477, which claims priority to U.S. Patent ApplicationSer. No. 61/383,927 filed on Sep. 17, 2010, the entire disclosures ofwhich are incorporated herein by reference.

TECHNICAL FIELD

The present invention relates to predicting proper narcotic usage;particularly, to a method and system for creating numerous controlledsubstance use indicators to predict the likelihood of a patientcorrectly using a prescription drug of interest.

BACKGROUND

Prescription drug abuse is one of the leading forms of drug abuse in theUS. The types of drugs most commonly abused today are narcotics,sedatives, and stimulants. There are other categories of drugs that canbe abused besides these three types, one such example being anabolicsteroids, and collectively these drugs are classified as controlledsubstances by the DEA. Narcotics have risen to the top of all controlledsubstances in terms of the number of people who abuse them.Approximately 3% of 12 year old children in the US admit to usingVicodin in the previous year, while about 15% of 18-25 year old men andwomen admit to the same. It is estimated that approximately 15 millionpeople in the US abuse prescription drugs. Emergency Departments haveseen a 111% increase in the number of visits from people who are seekingnarcotics for their addiction. Prescription drug abuse is the number onedrug abuse problem in the US.

Healthcare entities have to deal with this problem every day(pharmacists, hospitals, providers), as do law enforcement officials andeducators. One of the tools physicians, physician assistants,pharmacists, and law enforcement can use is a state based PrescriptionMonitoring Program, or PMP. One such example is available atohioPMP.org. All 50 states now have, or are developing, these programsand they are usually funded at the Federal level. These programs requirethat pharmacists and providers who dispense medications directly reportevery narcotic distribution to the state PMP. The state PMP maintains adatabase of these “transactions.” Approved providers can log into thestate PMP website and retrieve a patient's narcotic use information inPDF format. This document may be 1-10 pages long and annotates veryspecific details about prescription usage (who, where, when, what, howmuch, when written, when filled, new or refill, etc.). Presumably, aprovider, such as a physician, physician's assistant, or pharmacistwould utilize this site whenever they were concerned about the potentialfor prescription drug abuse. However, providers use this service at arelatively low rate because it is a somewhat arduous process to navigateto the site, login, enter demographic data, wait for the report search,download the PDF and then read all of the data. Ohio reports that only17% of prescribers in the state have even applied for access to the PMPand fewer than that use the system regularly.

SUMMARY

In an embodiment, a computer-implemented method for determining thelikelihood of proper prescription drug use by a patient comprisesobtaining a record from a prescription database, the record indicativeof a plurality of prescriptions corresponding to the patient, andgenerating, with one or more computer processors, a usage relatedindicator by comparing at least one of a prescription drug typecorresponding to two or more of the plurality of prescriptions or aprescription drug quantity corresponding to the two or more of theplurality of prescriptions with a plurality of general populationprescription drug use data. The method further comprises generating,with the one or more computer processors, an instruction relatedindicator by comparing a prescriber corresponding to at least one of theplurality of prescriptions to a plurality of general populationprescription drug instruction data, and combining, with the one or morecomputer processors, the usage related indicator and the instructionrelated indicator to produce a prescription drug use indicator fordisplay on a visual medium.

In another embodiment, a computer-implemented method for determining thelikelihood of proper prescription drug use by a patient comprisesobtaining a record from a prescription database on a server, the recordindicative of a plurality of prescriptions corresponding to the patient,a morphine equivalents unit percentile for a give morphine equivalentsunit period by comparing at least one of a narcotic type of two or moreof the plurality of prescriptions or a narcotic quantity of two or moreof the plurality of prescriptions with a plurality of general populationprescription drug use data. The method further comprises generating,with the one or more computer processors, an instruction relatedindicator including: a) identifying a potential prescription overlapsituation of at least two prescribers during a prescription overlapperiod to produce a prescription overlap percentile; and b) creating ofa prescriber indicator by comparing a prescriber quantity with theplurality of general population prescription drug use data to determinea prescriber percentile for a given prescriber period. Still further themethod comprises combining, with the one or more computer processors,the prescription overlap percentile, the prescriber percentile, and themorphine equivalents unit percentile to produce a narcotics useindicator for display on a visual medium.

BRIEF DESCRIPTION OF THE DRAWINGS

Without limiting the scope of the present method, system, and program,referring now to the drawings and figures:

FIG. 1 shows an illustrative chart, not to scale, showing the number ofpatients on the y-axis and the number of prescribers on the x-axis;

FIG. 2 shows an illustrative chart, not to scale, showing the naturallog of the number of patients on the y-axis and the number ofprescribers on the x-axis;

FIG. 3 shows an illustrative curve, not to scale, reflecting the data ofFIG. 2 and FIG. 3;

FIG. 4 shows a table representative of data that may be contained in apatient record;

FIG. 5 shows a schematic block diagram illustrating aspects of manyembodiments in a single diagram;

FIG. 6 shows a schematic diagram illustrating many potential usagerelated indicator embodiments;

FIG. 7 shows a schematic diagram illustrating many potential instructionrelated indicator embodiments;

FIG. 8 shows a schematic diagram illustrating many potential dispensingrelated indicator embodiments;

FIG. 9 shows a schematic diagram illustrating many potential auxiliaryindicator embodiments;

FIG. 10 shows a lower table representative of one embodiment's data thatis retrieved from a patient record, and an upper table representative ofseveral embodiments of indicators; and

FIG. 11 shows a lower table representative of one embodiment's data thatis retrieved from a patient record, and an upper table representative ofseveral embodiments of indicators.

These drawings are provided to assist in the understanding of exemplaryembodiments as described in more detail below and should not beconstrued as unduly limiting. In particular, the relative spacing,positioning, sizing and dimensions of the various elements illustratedin the drawings are not drawn to scale and may have been exaggerated,reduced or otherwise modified for the purpose of improved clarity. Thoseof ordinary skill in the art will also appreciate that a range ofalternative configurations have been omitted simply to improve theclarity and reduce the number of drawings.

DETAILED DESCRIPTION

The claimed method, system, and computer program product for determininga controlled substance use indicator enables a significant advance inthe state of the art. Of note, when the controlled substances ofinterest are narcotics, the result is a narcotics use indicator.Likewise, when sedatives or stimulants are the focus they result in asedative use indicator and a stimulant use indicator.

As previously touched upon, a prescription database (6000) may reside ona state PMP server, however one skilled in the art will appreciate thatthe prescription database (6000) described herein is not limited to astatewide system or a federal system, as it may be a hospital specificprescription database, a commercial prescription database, or communityspecific prescription database (6000). Similarly, the prescriptiondatabase (6000) need not reside on a server but rather may reside on alocal memory device in a standalone manner, and further, in anticipationof advances in health care IT infrastructure, the prescription database(6000) may be created for an individual patient broadly electronicallyquerying a network of health care providers and aggregating thecollected data, which may be completed in virtually real-time.Regardless of the scope, location, or creation of the prescriptiondatabase (6000), it contains at least one of record (6100) indicative ofthe prescription drug use by the patient. One illustrative record (6100)is seen in FIG. 4. A record (6100) may contain information such as apatient ID (6105), a prescription written date (6110), a prescriptionexpiration date (6115), a prescription period (6120), a prescriber(6145), a prescriber location (6150), a distributor (6155), adistributor location (6160), and a distribution date (6165). The record(6100) may even contain data indicative of the number of times it hasbeen accessed, such as a record request date (6200), data indicative ofwho has accessed the record such as a record requester (6300) datafield, as well as where the requester is located, such as a requesterlocation (6310) data field.

The record (6100) may also contain data pertaining to the prescriptionsthat have been filled for a particular patient, whether they are fornarcotics or other controlled substances. Therefore, the record (6100)may contain data about a prescribed narcotic such as a narcotic type(6125N), a narcotic strength (6130N), a narcotic form (6135N), and anarcotic quantity (6140N). The record (6100) may contain similarinformation regarding other prescribed controlled substances such as acontrolled substance type (6125C), a controlled substance strength(6130C), a controlled substance form (6135C), and a controlled substancequantity (6140C). Some controlled substance types potentiate each otherand become more dangerous when taken together. Two such examples arenarcotics and benzodiazepines. For example, the act of consuming anarcotic like demerol can become more dangerous by combining it with abenzodiazepine such as Lorazepam. Thus, in these examples the elementnumbers for narcotics end with the letter “N” and those for otherrelated controlled substances end in the letter “C”, while sharing thesame numerical references. These are simply examples of the data thatmay be contained within a record (6100) and are not all required, norare these the only types of data that may reside in a record (6100).

The present method, system, and computer program product retrievepatient specific data from a record (6100) and transforms the data intoat least one indicator by comparing the patient specific data with aplurality of general population prescription drug use data. Theindicator, or indicators, are then transformed into a controlledsubstance use indicator (10) via the application of at least oneadjustment factor. A diagram of one embodiment of the procedure is seenin FIG. 5 wherein at least one piece of patient specific data isretrieved from a record (6100) and is then transformed into at least oneof a usage related indicator (1000), an instruction related indicator(2000), a dispensing related indicator (3000), or an auxiliary indicator(4000) by comparing the patient specific data with a plurality ofgeneral population prescription drug use data. Then at least oneadjustment factor (5000) is applied to at least one of the indicators tocreate the narcotics use indicator (10).

In one embodiment patient specific data including at least a prescriber(6145), a distributor (6155), a narcotic type (6125N), a narcoticstrength (6130N), and a narcotic quantity (6140N) is retrieved from therecord (6100). Next, at least one prescription drug use processorreceives this data and transforms it into at least two indicators;namely, a usage related indicator (1000) and an instruction relatedindicator (2000). The usage related indicator (1000) is created bycomparing at least the patient information concerning the narcotic type(6125N), the narcotic strength (6130N), and the narcotic quantity(6140N) with a plurality of general population prescription drug usedata; while the instruction related indicator (2000) is created bycomparing at least the patient information concerning the prescriber(6145) with the plurality of general population prescription drug usedata.

The act of comparing patient specific data with the plurality of generalpopulation prescription drug use data can mean a number of things, aswill be explained in greater detail later. In the big picture thecomparison simply results in at least an indication of where the patientdata ranks when compared to similar data that is representative of alarger population of patients. For example, one embodiment may simplyidentify whether the patient data is in a below normal range, a normalrange, or an above normal range when compared to a larger population ofpatients. Alternatively, another embodiment may determine a percentileranking of the patient data compared to the larger population ofpatients.

Finally, at least one prescription drug use processor applies anadjustment factor (5000) to at least one of the usage related indicator(1000) and the instruction related indicator (2000) to create anadjusted indicator, and transforms the adjusted indicator into acontrolled substance use indicator (10) to display on a visual media.The controlled substance use indicator (10) is created within 5-10seconds of the request.

The embodiment above utilized only a usage related indicator (1000) andan instruction related indicator (2000). However, an example will beexplained with respect to FIGS. 10 and 11 and includes a discussion ofall the illustrated data and indicators for simplicity's sake only andthe presence of such in this explanation is not an indication that allthe data and indicators discussed are necessary. The lower table in FIG.10 represents patient specific data that has been acquired from a record(6100) in a prescription database (6000), however it should be notedthat for the previous embodiment it is not necessary that all of thispatient data is retrieved from the record (6100). This particularpatient had four prescriptions written between Feb. 18, 2010 and May 23,2010 and filled between Feb. 22, 2010 and May 27, 2010; two are for thenarcotic demerol and two are for the controlled substance lorazepam,which is why the demerol entry is labeled as 6125N while the lorazepamentry is labeled as 6125C (N for narcotic, C for controlled substance).

The upper table in FIG. 10 represents numerous indicators created fromthe patient data, as well as numerous adjustment factors (5000) used toarrive at the ultimate narcotics use indicator (10) appearing in theupper left corner of the figure as a NARx score. In one embodiment theusage related indicator (1000) may be a morphine equivalents unitindicator (1100). In this embodiment the narcotic type (6125N), thenarcotic strength (6130N), and the narcotic quantity (6140N) aretransformed into a morphine equivalents unit quantity (1120), seen inthe far right column of the lower table. The morphine equivalents unitquantity (1120) is then compared with the plurality of generalpopulation prescription drug use data to determine a morphineequivalents unit percentile (1140) for a given morphine equivalents unitperiod (1110). Thus, in the upper table of FIG. 10, within the “PeriodA” column, which corresponds to the morphine equivalents unit period(1110), the row labeled “Morphine” contains the morphine equivalentsunit quantity (1120), abbreviated MEU Qty in the table, on the left sideof the hash mark, and the morphine equivalents unit percentile (1140),abbreviated MEU % in the table, on the right side of the hash mark.

The upper table in FIG. 11 contains the actual values corresponding tothe lower table, illustrating that in the past 60 days, the morphineequivalents unit period (1110), from the reference date of Jul. 1, 2010,the morphine equivalents unit quantity (1120) prescribed is 450;although it should be noted that the morphine equivalents unit quantity(1120) is not limited to the amount prescribed during the period butrather could be the amount consumed during the period. In this specificexample, the morphine equivalents unit quantity (1120) places thispatient in the fifty-first percentile, which is the morphine equivalentsunit percentile (1140) displayed on the right side of the hash mark inthe upper table of FIG. 11.

With reference again to FIG. 10, the instruction related indicator(2000) may include the step of identifying a potential prescriptionoverlap situation when the record (6100) includes at least twoprescribers (6145) during a prescription overlap period (2310). Aprescription overlap indicator (2300) may then be created by determininga prescription overlap quantity (2320) that is the total number of daysthat the prescription period (6120) of the each prescriber (6145)coincide. Further, comparison of the prescription overlap quantity(2320) with the plurality of general population prescription drug usedata yields a prescription overlap percentile (2340). For example, whenthe prescription overlap period (2310) is 60 days from the referencedate of Jul. 1, 2010, as in FIG. 11, there were three prescriptions openwithin that period, only two of which are for narcotics. Therefore,within the prescription overlap period (2310) of 60 days, there were 8days, namely May 1st through May 8th, in which the two narcoticprescriptions for demerol overlapped. Therefore, the prescriptionoverlap quantity (2320) is 8, as seen in the upper table of FIG. 11,which puts this patient in the eighteenth percentile, which is theprescription overlap percentile (2340). A high prescription overlapquantity (2320), or prescription overlap percentile (2340), isindicative of likely improper prescription drug use, particularly incases where the prescription overlap quantity (2320) includes days inwhich a patient had multiple open prescriptions for the same narcoticoriginating from different prescribers. The prescription overlapindicator (2300) may be applied only to narcotic prescriptions, only tocontrolled substance prescriptions, or to both.

Another possible usage related indicator (1000) is an associatedcontrolled substance unit indicator (1300). The associated controlledsubstance unit indicator (1300) is created in part by comparing theassociated controlled substance quantity (6140C) with the plurality ofgeneral population prescription drug use data to determine a controlledsubstance unit percentile (1340) for a given controlled substance unitperiod (1310). Thus, in the upper table of FIG. 10, within the “PeriodA” column, which corresponds to the controlled substance unit period(1310), the row labeled “Controlled” contains the associated controlledsubstance unit quantity (1320), abbreviated CTRL Sub Qty in the table,on the left side of the hash mark, and the controlled substance unitpercentile (1340), abbreviated CTRL Sub % in the table, on the rightside of the hash mark.

The upper table in FIG. 11 contains the actual values corresponding tothe lower table, illustrating that in the past 60 days, the associatedcontrolled substance unit period (1310), from the reference date of Jul.1, 2010, the controlled substance unit quantity (1320) prescribed is 90;although it should be noted that the controlled substance unit quantity(1320) is not limited to the amount prescribed during the period butrather could be the amount consumed during the period. In this specificexample, the controlled substance unit quantity (1320) places thispatient in the ninety-fifth percentile, which is the controlledsubstance unit percentile (1340) displayed on the right side of the hashmark in the upper table of FIG. 11.

Another possible instruction related indicator (2000) is a prescriberindicator (2200). The creation of a prescriber indicator (2200) iscreated in part by comparing a prescriber quantity (2220) with theplurality of general population prescription drug use data to determinea prescriber percentile (2240) for a given prescriber period (2210).Thus, in the upper table of FIG. 10, within the “Period A” column, whichcorresponds to the prescriber period (2210), the row labeled“Prescribers” contains the prescriber unit quantity (2220), abbreviatedPrescriber Qty in the table, on the left side of the hash mark, and theprescriber percentile (2240), abbreviated Prescriber % in the table, onthe right side of the hash mark.

The upper table in FIG. 11 contains the actual values corresponding tothe lower table, illustrating that in the past 60 days, the prescriberperiod (2210), from the reference date of Jul. 1, 2010, the prescriberquantity (2220) is 2. In this specific example, the prescriber quantity(2220) places this patient in the thirty-three percentile, which is theprescriber percentile (2240) displayed on the right side of the hashmark in the upper table of FIG. 11.

In addition to the usage related indicator (1000) and the instructionrelated indicator (2000), the method may incorporate a dispensingrelated indicator (3000). The dispensing related indicator (3000) iscreated by comparing at least the patient information concerning thedistributor (6155) with the plurality of general population prescriptiondrug use data, and in this embodiment the adjustment factor (5000) isthen applied to at least one of the usage related indicator (1000), theinstruction related indicator (2000), and the dispensing relatedindicator (3000).

In one particular embodiment the dispensing related indicator (3000) isa distribution source indicator (3100). The creation of a distributionsource indicator (3100) is created in part by comparing a distributionsource quantity (3120) with the plurality of general populationprescription drug use data to determine a distribution source percentile(3140) for a given distribution source period (3110). Thus, in the uppertable of FIG. 10, within the “Period A” column, which corresponds to thedistribution source period (3110), the row labeled “Pharmacies” containsthe distribution source quantity (3120), abbreviated Dist Source Qty inthe table, on the left side of the hash mark, and the distributionsource (3140), abbreviated Dist Source % in the table, on the right sideof the hash mark.

The upper table in FIG. 11 contains the actual values corresponding tothe lower table, illustrating that in the past 60 days, the distributionsource period (3310), from the reference date of Jul. 1, 2010, thedistribution source quantity (3120) is 1. In this specific example, thedistribution source quantity (3120) places this patient in the twentiethpercentile, which is the distribution source percentile (3140) displayedon the right side of the hash mark in the upper table of FIG. 11.

Now that the first data column associated with the five rows of data inthe upper tables of FIGS. 10 and 11 have been discussed, severaladditional steps will be explained; however, additional indicators willbe discussed later. It should be noted again that all five indicators(2220, 3120, 1120, 1320, 2320) of these two figures are not required,rather this is merely one illustrative embodiment being explained indetail. In these figures three types of indicators have been examined,namely two usage related indicators (1000) including a morphineequivalents unit indicator (1100) and an associated controlled substanceunit indicator (1300), two instruction related indicators (2000)including a prescriber indicator (2200) and a prescription overlapindicator (2300), and one dispensing related indicator (3000) that was adistribution source indicator (3100). As previously mentioned, anadjustment factor (5000) may be applied to any, or all, of theseindicators to weight their relevance in predicting proper prescriptiondrug use and ultimately arrive at a narcotics use indicator (10).

With specific reference to the embodiment of FIGS. 10 and 11 again, theadjustment factor (5000) is seen in the right column of the uppertables. In this embodiment each of the usage related indicators (1000)have a usage adjustment factor (5100), each of the instruction relatedindicators (2000) have an instruction adjustment factor (5200), and thedispensing related indicator (3000) has a dispensing adjustment factor(5300). Even further, as seen in the right column of the upper table ofFIG. 11, in this one embodiment, the morphine equivalents unit indicator(1100) has a narcotic usage adjustment factor (5110), the controlledsubstance unit indicator (1300) has a controlled substance usageadjustment factor (5120), the prescriber indicator (2200) has aprescriber adjustment factor (5210), the prescription overlap indicator(2300) has an overlap adjustment factor (5220), and the distributionsource indicator (3100) has a dispensing adjustment factor (5300). Herethe narcotic usage adjustment factor (5110) is four times greater thanthe other adjustment factors because the morphine equivalents unitpercentile (1140) is more directly indicative of overall prescriptiondrug use.

Referring now to FIG. 11 and focusing only on the “60 Day” column andthe “Wt.” column, a narcotics use indicator (10) can be developed forthis single period. For example, the narcotics use indicator (10) may besimply a weighted average of the five percentile values (2240, 3140,1140, 1340, 2340). In this case, taking the sum of the prescriberpercentile (2240) multiplied by the prescriber adjustment factor (5210),plus the distribution source percentile (3140) multiplied by thedispensing adjustment factor (5300), plus the morphine equivalents unitpercentile (1140) multiplied by the narcotic usage adjustment factor(5110), plus the controlled substance unit percentile (1340) multipliedby the controlled substance usage adjustment factor (5120), plus theprescription overlap percentile (2340) multiplied by the overlapadjustment factor (5220); and dividing that sum by the sum of all theadjustment factors (5210, 5300, 5110, 5120, 5220) produces a number thatis effectively a weighted percentile. In this example, the result wouldbe [(33*1)+(20*1)+(51*4)+(95*1)+(18*1)]/(1+1+4+1+1)=46.25. For theconvenience of a treating prescriber that requested the narcotics useindicator (10) this number may then be rounded to the nearest wholenumber which in this case is 46. In a further embodiment, it is likelythat the treating prescriber would also like to immediately know thenumber of currently active prescriptions, yet still have a singleconvenient reference number, or score, to represent the likelihood ofprescription drug abuse. Therefore, in this further embodiment, thenumber of active prescriptions is an active prescription indicator(4300) and is added as a third digit in the narcotics use indicator(10). In the example of FIG. 11, there are no active prescriptions, sothe active prescription indicator (4300) is 0, which is applied to theend of the weighted percentile previously calculated to be 46 to arriveat a three digit narcotics use indicator (10) of 460. A treatingprescriber can easily look at this narcotics use indicator (10) andquickly assess the likelihood that this particular patient is going tocorrectly utilize a prescription for a narcotic medication and/or acontrolled substance. In this embodiment a patient with 9 or more activeprescriptions would receive a three digit narcotics use indicator (10)of 469, which would immediately draw the attention of the prescriber,possibly warranting a more detailed review of the patient's prescriptiondrug use. Thus, past patient prescription drug use data is transformedinto a numerical narcotics use indicator (10) displayed on a visualmedia. The visual media may be a Cathode Ray Tube (CRT) monitor, aLiquid Crystal Display (LCD) monitor, a plasma monitor, a projector andscreen, paper, and/or any other such visual display device known tothose of ordinary skill in the art.

While the example above focused on a single period of time, FIG. 11illustrates that the values just determined above may be determined formultiple periods. The upper table of FIG. 11 illustrates one embodimentin which 4 such periods are utilized. In such an embodiment, multiperiod percentiles may be determined for each indicator. Specifically,the “AVG” column of the table illustrates a multi period prescriberpercentile (2250), a multi period distribution source percentile (3150),a multi period morphine equivalents unit percentile (1150), a multiperiod controlled substance percentile (1350), and a multi periodprescriber overlap percentile (2350). In this embodiment, each of thesemulti period percentiles are simply the average percentile value for thegiven number of periods. Thus, the multi period prescriber percentile(2250) is simply the sum of the four individual period specificprescriber percentiles (2240) divided by the number of periods, in thiscase four, leading to (33+38+28+20)/4=29.75; and likewise for the otherfour indicators. Thus, the adjustment factors (5000) may be applied tothese multi period percentiles in exactly the same manner as previouslydiscussed to arrive at a weighted percentile. In this example, theresult would be[(29.75*1)+(26.75*1)+(70.25*4)+(64*1)+(16.25*1)]/(1+1+4+1+1)=52.29. Forthe convenience of a treating prescriber that requested the narcoticsuse indicator (10) this number may then be rounded to the nearest wholenumber which in this case is 52. Then in the embodiment incorporatingthe active prescription indicator (4300), which remains at 0, the threedigit narcotics use indicator (10) would be 520, as seen in FIG. 11.Therefore, in this particular example, looking at a two year time spanrather than just a two month period raises the three digit narcotics useindicator (10) from 460 to 520. Obviously the lower table of FIG. 11 hasbeen abbreviated and does not contain all of the prescriptions requiredto calculate the data for the 180 day period, the 365 day period, andthe 730 day period, but the procedure is the same as just reviewed forthe 60 day period.

A benefit of incorporating multiple periods is that because all theperiods may have the same start date, i.e. the reference date in FIGS.10 and 11, the data contained in the first period is also included in asecond period, and likewise the data in the third period includes thatin the first and the second period, and likewise the data in the fourthperiod includes that in the first period, second period, and thirdperiod. Therefore, in one embodiment of FIG. 11, namely when all fourperiods are considered, the morphine equivalents unit quantity (1120) of450 when the morphine equivalents unit period (1110) is 60 days, is alsoincluded in the morphine equivalents unit quantity (1120) when themorphine equivalents unit period (1110) is 180 days, 365 days, and 730days. Therefore, in this embodiment the multi period morphineequivalents unit percentile (1150) is the average of the four periodswherein each period includes the morphine equivalents unit quantity(1120) from the first period; thus, the most recent data values arepreferentially weighted. Although this preferential weighting isdescribed above with respect to the multi period morphine equivalentsunit percentile (1150), it may be applied to the determination of amulti period narcotic unit percentile (1250), a multi period controlledsubstance unit percentile (1350), a multi period prescription percentile(2150), a multi period prescriber percentile (2250), a multi periodprescription overlap percentile (2350), a multi period distributionsource percentile (3150), or a multi period distribution geographypercentile (3250).

In another embodiment any of the adjustment factors may be automaticallyadjusted if preset criteria are met concerning data that highlycorrelates with improper prescription drug use. For example, aspreviously discussed with respect to FIG. 11, patients that have aprescription overlap quantity (2320) including days in which a patienthad multiple open prescriptions for the same narcotic originating fromdifferent prescribers may flag an automatic adjustment to the overlapadjustment factor (5220) of at least twice the normal overlap adjustmentfactor (5220). Likewise, in another embodiment the prescriber adjustmentfactor (5210) may be automatically adjusted by a factor of at least twoif a patient holds onto prescriptions from the same prescriber and thenhas them filled so that at least two controlled substance prescriptionsare open at the same time based upon prescriptions for the samecontrolled substance by a single prescriber.

As seen in FIG. 7, yet another possible instruction related indicator(2000) is a prescription indicator (2100). The creation of aprescription indicator (2100) is created in part by comparing aprescription quantity (2120) with the plurality of general populationprescription drug use data to determine a prescription percentile (2140)for a given prescription period (2110). Thus, one with skill in the artwill recognize that this prescription indicator (2100) is yet anotherindicator that may be found in the upper tables of FIGS. 10 and 11 andweighted in the same manner previously discussed with respect to theother indicators to influence the narcotics use indicator (10). As such,the “Period A” column, would correspond to the prescription period(2110), and a row labeled “Prescriptions” would contain the prescriptionunit quantity (2120) on the left side of a hash mark, and theprescription percentile (2140) on the right side of the hash mark.Similarly, the instruction adjustment factor (5200) may include aprescription adjustment factor to weight the significance of theprescription indicator (2100) in the narcotics use indicator (10).

Further, as seen in FIG. 6, another possible usage related indicator(1000) is a narcotic unit indicator (1200). The narcotic unit indicator(1200) is created in part by comparing the narcotic quantity (6140N)with the plurality of general population prescription drug use data todetermine a narcotic unit percentile (1240) for a given narcotic unitperiod (1210). Thus, one with skill in the art will recognize that thisnarcotic unit indicator (1200) is yet another indicator that may befound in the upper tables of FIGS. 10 and 11 and weighted in the samemanner previously discussed with respect to the other indicators toinfluence the narcotics use indicator (10). As such, the “Period A”column, would correspond to the prescription period (2110), and a rowlabeled “Narcotics” would contain the narcotic unit quantity (1220) onthe left side of a hash mark, and the narcotic unit percentile (1240) onthe right side of the hash mark. Similarly, the usage adjustment factor(5100) may include a narcotic unit adjustment factor to weight thesignificance of the narcotic unit indicator (1200) in the narcotics useindicator (10).

As seen in FIG. 8, another embodiment includes a dispensing relatedindicator (3000) that is a distribution geography indicator (3200). Thecreation of a distribution geography indicator (3200) is created in partby comparing a distribution geography distance (3220) with the pluralityof general population prescription drug use data to determine adistribution geography percentile (3240) for a given distributiongeography period (3210). Thus, one with skill in the art will recognizethat this distribution geography indicator (3200) is yet anotherindicator that may be found in the upper tables of FIGS. 10 and 11 andweighted in the same manner previously discussed with respect to theother indicators to influence the narcotics use indicator (10). As such,the “Period A” column, would correspond to the distribution geographyperiod (3210), and a row labeled “Geography” would contain thedistribution geography distance (3220) on the left side of a hash mark,and the distribution geography percentile (3240) on the right side ofthe hash mark. Similarly, the dispensing adjustment factor (5300) mayinclude a distribution geography adjustment factor to weight thesignificance of the distribution geography indicator (3200) in thenarcotics use indicator (10). In one embodiment the distributiongeography distance (3220) is the total distance between the patient'shome address and the location of the pharmacy, or pharmacies, that fillsprescriptions during the distribution geography period (3210). Inanother embodiment the distribution geography distance (3220) is thedistance between the locations of the pharmacies that fill prescriptionsduring the distribution geography period (3210). In yet anotherembodiment the distribution geography distance (3220) is the distancebetween the locations of only pharmacies that fill prescriptions fordrugs within the same family during the distribution geography period(3210).

As seen in FIGS. 5 and 9, another embodiment includes an auxiliaryindicator (4000) that is a controlled substance request indicator(4100), The controlled substance request indicator (4100) is created inpart by comparing the number of times, a controlled substance requestquantity (4120), that a patient has had a narcotics use indicator (10)requested by a prescriber during a given period, namely a controlledsubstance request period (4110). The controlled substance requestquantity (4120) may then be compared with the plurality of generalpopulation prescription drug use data to determine a controlledsubstance request percentile (4240) for the given controlled substancerequest period (4110). Thus, one with skill in the art will recognizethat this controlled substance request indicator (4100) is yet anotherindicator that may be found in the upper tables of FIGS. 10 and 11 andweighted in the same manner previously discussed with respect to theother indicators to influence the narcotics use indicator (10). As such,the “Period A” column, would correspond to the controlled substancerequest period (4110), and a row labeled “controlled substance requests”would contain the controlled substance request quantity (4120) on theleft side of a hash mark, and the NAR request percentile (4140) on theright side of the hash mark. Similarly, an auxiliary indicatoradjustment factor (5400) may include a controlled substance requestadjustment factor to weight the significance of the controlled substancerequest indicator (4100) in the narcotics use indicator (10). In anotherembodiment, this controlled substance request adjustment factor may beautomatically increased if the amount of narcotics use indicator (10)requests has exceeded a preset normal number of requests.

Even further, another embodiment includes an auxiliary indicator (4000)that is a controlled substance rate of change indicator (4200). Thecontrolled substance rate of change indicator (4200) is created in partby comparing how the patient's narcotics use indicator (10) has changedover a period, or periods, of time to the rate of change associated withthe plurality of general population prescription drug use data. Forexample, a request for a narcotics use indicator (10) may result in thedetermination of a first narcotics use indicator at a fixed timeinterval prior to the request date, and then the determination of asecond narcotics risk indicator at a rate of change period (4210) priorto the fixed time interval. The difference between the first and secondnarcotics risk indicators, referred to as a controlled substancevariation (4220), may then be used to adjust the presently requestednarcotics use indicator if a threshold controlled substance variation(4220) is exceeded. The controlled substance variation (4220) may becompared with the plurality of general population prescription drug usedata to determine a rate of change percentile (4240) for the given rateof change period (4210). Thus, one with skill in the art will recognizethat this controlled substance rate of change indicator (4200) is yetanother indicator that may be found in the upper tables of FIGS. 10 and11 and weighted in the same manner previously discussed with respect tothe other indicators to influence the presently requested narcotics useindicator (10). As such, the “Period A” column, would correspond to therate of change period (4210), and a row labeled “Rate of Change” wouldcontain the controlled substance variation (4220) on the left side of ahash mark, and the rate of change percentile (4240) on the right side ofthe hash mark. Similarly, the auxiliary indicator adjustment factor(5400) may include a rate of change adjustment factor to weight thesignificance of the controlled substance rate of change requestindicator (4200) in the narcotics use indicator (10). In anotherembodiment, this controlled substance rate of change adjustment factormay be automatically increased if the controlled substance variation(4220) has exceeded a preset normal number of requests.

Throughout this document there are multiple references to a step ofcomparing a quantity, whether it is the 1120, 1220, 1320, 2120, 2220,2320, 3120, 3220, 4120, or 4220 quantity, “with the plurality of generalpopulation prescription drug use data” to determine an indicator,whether it be a usage related indicator (1000), an instruction relatedindicator (2000), or a dispensing related indicator (3000). In some ofthe many disclosed embodiments the determination of an indicatorincludes a determination of whether the quantity is within an acceptablerange or an unacceptable range, however other embodiments determineapproximate percentile rankings of the quantity compared to the generalpopulation data, such as the 1140, 1240, 1340, 2140, 2240, 2340, 3140,3240, 4140, or 4240 percentiles.

The general population prescription drug use data referenced is dataassociated with at least 1000 patients over the period of interest. Inone embodiment this general population data is present in the database(6000) and is extracted for use in arriving at the indicators, or insome embodiments the percentile(s). The general population data need notbe extracted each time patient specific data is retrieved from thedatabase (6000); rather the general population data may be extractedafter extended intervals, which may be months or even years. The generalpopulation prescription drug use data may be from a statewide or federalprescription database, a hospital specific prescription database, acommercial prescription database, or community specific prescriptiondatabase. Thus, in yet another embodiment the general populationprescription drug use data referenced is data associated with at least1,000,000 patients over the period of interest; while yet a furtherembodiment, such as data used in generating FIGS. 10 and 11, utilizesdata associated with at least 5,000,000 patients over the period ofinterest.

Therefore, the act of comparing a quantity “with the plurality ofgeneral population prescription drug use data” to determine an indicatormay include the step of previously acquiring the general populationprescription drug use data, processing the data, converting the datainto a quickly accessible electronic format, and storing the converteddata on hardware for use in determining the final narcotics riskindicator (10) in less than 5 seconds, whether the general populationprescription drug data is local or on a hardware device on the otherside of the planet. Thus, in one embodiment a local prescription druguse processor securely retrieves and stores into memory patient specificdata from a remote database (6000), the local prescription drug useprocessor securely retrieves and stores into memory previously compiledand transformed data representative of the general populationsprescription drug use, the local prescription drug use processorretrieves portions of this stored data to form and store at least ausage related indicator (1000) and an instruction related indicator(2000), the local prescription drug use processor applies an adjustmentfactor (5000) to at least one of usage and instruction relatedindicators (1000, 2000) and transforms them into a numerical narcoticsuse indicator (10), and the local prescription drug use processorformats and transmits the narcotics use indicator (10) to display on avisual media. Further, in light of confidential patient data security,the local prescription drug use processor may then clear the patientspecific data from the local memory, as well as leave a timestamp withinthe remote database (6000) to serve as an indicator of when a patient'sdata was accessed. The prescription drug use processor may furthersecurely transmit the narcotics use indicator (10) back to the database(6000) for storage and retrieval during subsequent data requests indetermining updated narcotics use indicators (10). Thus, a system forcarrying out the determination of a narcotics use indicator (10) mayconsist of several securely connected pieces of hardware communicatingwith the specially programmed prescription drug use processor todetermine the narcotics use indicator (10). As the local prescriptiondrug use processor retrieves the patient specific data from the database(6000), it may create a local patient-specific database for temporarilystoring and processing data. The local patient-specific database iscleared of patient specific data upon the creation of the narcotics useindicator (10) and any associated reports that are simultaneouslycreated.

The analysis of large quantities of data is well known in the field ofstatistics to identify acceptable ranges, unacceptable ranges, andpercentile rankings, and therefore will not be reviewed in detail.However, one of many embodiments will be discussed for illustrativepurposes. For instance, FIG. 1 illustrates raw data concerning thenumber of people in the general population drug use data on the y-axis,and the number of prescribers for a given period across the x-axis. Itis clear from this figure that during this particular period, theoverwhelming majority of patients only fill prescriptions from a singleprescriber. A further embodiment determines a log normal distribution ofthe data, as seen in FIG. 2, which has the effect of straightening outthe curve and spreading out the values. In this example, a log normaldistribution may be preferred because using the raw data only would puta very small quantity of prescribers at the 99th percentile. This wouldmean that above this very small quantity of prescribers there would beno differentiation among patients. In yet another embodiment the rawdata, or the log natural data, may be used to create a curve from whicha percentile value is easily determined, as seen in FIG. 3. For example,the area under the curve seen in FIG. 3 and to the left of the linelabeled “A” puts this number of prescribers in the 15th percentile,whereas the position of the line labeled “B” puts this number ofprescribers in the 90th percentile. FIGS. 6-9 schematically illustratesimilar analysis of data to produce one or more of the following curves,and one or more of the following percentiles; namely, morphineequivalents unit curve (1130), morphine equivalents unit percentile(1140), narcotic unit curve (1230), narcotic unit percentile (1240),controlled substance unit curve (1330), controlled substance unitpercentile (1340), prescription curve (2130), prescription percentile(2140), prescriber curve (2230), prescriber percentile (2240),prescription overlap curve (2330), prescription overlap percentile(2340), distribution source curve (3130), distribution source percentile(3140)

The prescription drug use processor is a specially programmed computerdevice such as a personal computer, a portable phone, a multimediareproduction terminal, a tablet, a PDA (Personal Digital Assistant), ora dedicated portable terminal that can perform the secure retrieval andprocessing of input, output, storage and the like of information. Itgoes without saying that such a program can be distributed through arecording medium such as a CD-ROM and a transmission medium such as theInternet. Further, the present invention may be a computer-readablerecording medium such as a flexible disk, a hard disk, a CD-ROM, an MO,a DVD, a DVD-ROM, a DVD-RAM, a BD (Blu-ray Disc), flash drives, thumbdrives, and a semiconductor memory that records the computer program.Thus, the distributed program may be used to program a computer tocreate a prescription drug processor thereby becoming a special purposecomputer to securely perform particular functions pursuant toinstructions from program software.

Numerous alterations, modifications, and variations of the preferredembodiments disclosed herein will be apparent to those skilled in theart and they are all anticipated and contemplated to be within thespirit and scope of this application. For example, although specificembodiments have been described in detail, those with skill in the artwill understand that the preceding embodiments and variations can bemodified to incorporate various types of substitute and or additional oralternative steps, procedures, and the order for such steps andprocedures. Accordingly, even though only few variations of the presentmethodology and system are described herein, it is to be understood thatthe practice of such additional modifications and variations and theequivalents thereof, are within the spirit and scope of thisapplication. The corresponding structures, materials, acts, andequivalents of all methods, means, and step plus function elements inthe claims below are intended to include any structure, material, oracts for performing the functions in combination with other claimedelements as specifically claimed.

We claim:
 1. A computer-implemented method for determining thelikelihood of proper prescription drug use by a patient, the methodcomprising: obtaining a record from a prescription database, the recordindicative of a plurality of prescriptions corresponding to the patient;generating, with one or more computer processors, a usage relatedindicator by comparing at least one of a prescription drug typecorresponding to two or more of the plurality of prescriptions or aprescription drug quantity corresponding to the two or more of theplurality of prescriptions with a plurality of general populationprescription drug use data; generating, with the one or more computerprocessors, an instruction related indicator by comparing a prescribercorresponding to at least one of the plurality of prescriptions to theplurality of general population prescription drug use data; combining,with the one or more computer processors, the usage related indicatorand the instruction related indicator, including applying at least onenumerical adjustment factor to one or both of the usage relatedindicator and the instruction related indicator, to produce aprescription drug use indicator; and displaying the prescription druguse indicator on a visual medium.
 2. The method of claim 1, wherein thedrug use indicator is at least a two digit number.
 3. The method ofclaim 1, wherein the drug use indicator is at least a three digitnumber, wherein a last digit is an active prescription indicator andrepresents a number of active prescriptions.
 4. The method of claim 1,wherein the plurality of prescriptions includes at least oneprescription for a narcotic; wherein, for the at least one prescriptionfor a narcotic, the prescription drug type is a narcotic type and theprescription drug quantity is a narcotic quantity; and whereingenerating the usage related indicator includes the creation of amorphine equivalents unit indicator by transforming the narcotic type, anarcotic strength, and the narcotic quantity into a morphine equivalentsunit quantity, and comparing the morphine equivalents unit quantity withthe plurality of general population prescription drug use data todetermine a morphine equivalents unit percentile for a given morphineequivalents unit period.
 5. The method of claim 4, wherein, in combiningthe usage related indicator and the instruction related indicator, oneadjustment factor that is applied is a narcotic usage weighting factor.6. The method of claim 5, wherein the morphine equivalents unitpercentile is determined for at least two morphine equivalents unitperiods and the narcotic usage weighting factor is applied to a multiperiod morphine equivalents unit percentile that is the average of themorphine equivalents unit percentiles.
 7. The method of claim 1, whereinthe plurality of prescriptions includes at least one prescription for acontrolled substance; wherein, for the at least one prescription for acontrolled substance, the prescription drug type is a controlledsubstance type and the prescription drug quantity is a controlledsubstance quantity; and wherein generating the usage related indicatorincludes the creation of a controlled substance unit indicator bycomparing the controlled substance quantity with the plurality ofgeneral population prescription drug use data to determine a controlledsubstance unit percentile for a given controlled substance unit period.8. The method of claim 7, wherein, in combining the usage relatedindicator and the instruction related indicator, one adjustment factorthat is applied is a controlled substance usage weighting factor.
 9. Themethod of claim 8, wherein the controlled substance unit percentile isdetermined for at least two controlled substance unit periods and thecontrolled substance usage weighting factor is applied to a multi periodcontrolled substance unit percentile that is the average of thecontrolled substance unit percentiles.
 10. The method of claim 1,wherein the record contains a prescription period, and whereingenerating the instruction related indicator includes identifying apotential prescription overlap situation when the record includes atleast two prescribers during a prescription overlap period, and creatingof a prescription overlap indicator by determining a prescriptionoverlap quantity that is the total number of days that the prescriptionperiod of the each prescriber coincide and comparing the prescriptionoverlap quantity with the plurality of general population prescriptiondrug use data to determine a prescription overlap percentile.
 11. Themethod of claim 10, wherein, in combining the usage related indicatorand the instruction related indicator, one adjustment factor that isapplied is an overlap weighting factor.
 12. The method of claim 11,wherein the prescription overlap percentile is determined for at leasttwo prescription overlap periods and the overlap weighting factor isapplied to a multi period prescription overlap percentile that is theaverage of the prescription overlap percentiles.
 13. The method of claim1, wherein generating the instruction related indicator includes thecreation of a prescriber indicator that indicates a ranking of aprescriber quantity, which is a number of prescribers identified in therecord for the patient for a given time period, compared to theplurality of general population prescription drug use data to determinea prescriber percentile.
 14. The method of claim 13, wherein, incombining the usage related indicator and the instruction relatedindicator, one adjustment factor that is applied is a prescriberweighting factor.
 15. The method of claim 14, wherein the prescriberpercentile is determined for at least two prescriber periods and theprescriber weighting factor is applied to a multiperiod prescriberpercentile that is the average of the prescriber percentiles.
 16. Acomputer-implemented method for determining the likelihood of properprescription drug use by a patient, the method comprising: obtaining arecord from a prescription database, the record indicative of aplurality of prescriptions corresponding to the patient, and each of theplurality of prescriptions including an identification of a distributor;generating, with one or more computer processors, a usage relatedindicator by comparing at least one of a prescription drug typecorresponding to two or more of the plurality of prescriptions or aprescription drug quantity corresponding to the two or more of theplurality of prescriptions with a plurality of general populationprescription drug use data; generating, with the one or more computerprocessors, an instruction related indicator by comparing a prescribercorresponding to at least one of the plurality of prescriptions to theplurality of general population prescription drug use data; generating,with the one or more computer processors, a dispensing related indicatorthat indicates a ranking of the distributor for each prescriptioncompared to the plurality of general population prescription drug usedata, combining, with the one or more computer processors, the usagerelated indicator, the instruction related indicator, and the dispensingrelated indicator, including applying at least one numerical adjustmentfactor to at least one of the usage related indicator, the instructionrelated indicator, and the dispensing related indicator, to produce aprescription drug use indicator; and displaying the prescription druguse indicator on a visual medium.
 17. The method of claim 16, whereinthe narcotics use indicator is at least a two digit number.
 18. Themethod of claim 16, wherein the narcotics use indicator is at least athree digit number, wherein a last digit is an active prescriptionindicator and represents a number of active prescriptions.
 19. Acomputer-implemented method for determining the likelihood of properprescription drug use by a patient, the method comprising: obtaining arecord from a prescription database on a server, the record indicativeof a plurality of prescriptions corresponding to the patient;generating, with one or more computer processors, a morphine equivalentsunit percentile for a given morphine equivalents unit period bycomparing at least one of a narcotic type of two or more of theplurality of prescriptions or a narcotic quantity of two or more of theplurality of prescriptions with a plurality of general populationprescription drug use data; generating, with the one or more computerprocessors, an instruction related indicator including: a) identifying apotential prescription overlap situation of at least two prescribersduring a prescription overlap period to produce a prescription overlappercentile; and b) creating of a prescriber indicator by comparing aprescriber quantity, which is a number of prescribers in the record ofthe patient, with the plurality of general population prescription druguse data to determine a prescriber percentile for a given prescriberperiod; combining, with the one or more computer processors, theprescription overlap percentile, the prescriber percentile, and themorphine equivalents unit percentile to produce a narcotics useindicator; and displaying the narcotics use indicator on a visualmedium.
 20. The method of claim 19, wherein the narcotics use indicatoris at least a two digit number.
 21. The method of claim 19, wherein thenarcotics use indicator is at least a three digit number, wherein a lastdigit is an active prescription indicator and represents a number ofactive prescriptions.